Predicting skin age based on the analysis of skin flora and lifestyle data

ABSTRACT

The present invention relates to a combination of experimental and computational workflows that allow characterization of specific molecular mechanisms by which the microbiome contribute to skin health and skin age.

RELATED APPLICATION DATA

This application is a continuation of U.S. application Ser. No.15/760,813, filed Mar. 16, 2018, now U.S. Pat. No. 10,679,725, issuedJun. 9, 2020, which is a U.S. National Phase of International PatentApplication PCT/US2016/052161, filed Sep. 16, 2016, which claims thebenefit of priority under 35 U.S.C. § 119(e) of U.S. Provisional PatentApplication Ser. No. 62/220,072, filed Sep. 17, 2015, the entirecontents of which is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The invention relates generally to computational methods and morespecifically to methods and a system for characterizing skin age as afunction of skin flora.

Background Information

About 100 trillion microorganisms live in and on the human body vastlyoutnumbering the body's approximately 10 trillion human cells. Thesenormally harmless viruses, bacteria and fungi are referred to ascommensal or mutualistic organisms. Commensal and mutualistic organismshelp keep our bodies healthy in many ways: they help us to digest foodsand acquire nutrients such as vitamins B and K, encourage the immunesystem to develop and prevent the colonization of, for example,bacterial pathogens that cause disease by competing with them. Togetherall of the microorganisms living in and on the body—commensal,mutualistic and pathogenic—are referred to as the microbiome and theirequilibrium and associated metabolome is closely linked to anindividual's health status and vice-versa.

Next generation sequencing (NGS) has created an opportunity to quicklyand accurately identify and profile the microbiome inhabiting the skinand subcutaneous tissue. The optimal flora also interacts with the hostimmune system in a synergistic way further propagating its healthbenefits. The associated metabolome of individuals can also be profiledeither by a mass-spectrometry based system or using genomics-basedmetabolome modeling and flux-balance analysis and used to make a healthymetabolome profile. All these methodologies can be used to dissect thecomplexity of microbial communities.

The highly dynamic microbial communities than live on the skin areimportant to skin health. While the importance of skin microbiome makesit an appealing target for promoting skin health, this inherentvariability in these communities makes it difficult to identify theunderlying molecular mechanisms that link microbiome structure to humanfitness. One possible reason for this high level of population diversityis that there is a significant functional redundancy in the population.While a large variety of possible population structures may befunctionally equivalent in their aggregate metabolic capacities, thespecific assembly of molecular functions would be the key indicator of amicrobial community's capacity to influence human host state.

Aging is the accumulation of changes in an organism or object over time.Aging in humans refers to a multidimensional process of physical,biological, psychological, and social change. One of the human organswidely studied within the context of aging is skin. Human skin ages overtime, but the specifics of that process, the pace, and extent variesdrastically among different individuals, and is a complex interplaybetween genetic elements, and environmental factors, includingmicrobiome and lifestyle characteristics. Understanding this dynamic iscritical for better controlling the aging process.

SUMMARY OF THE INVENTION

The invention relates generally to identifying the specific molecularmechanisms within which microbiome contributes to skin health. To thisend, a unique and richly contextualized dataset of skin microbiomes hasbeen assembled for analysis. Using computational biology and machinelearning techniques, molecular information from population structuredata are extrapolated and the information is used to identify theimportant links between microbiome and skin health.

Accordingly, in one embodiment, the invention provides a method ofidentifying the specific molecular mechanisms within which microbiomecontributes to skin age. To this end, a unique and richly contextualizeddataset of skin microbiomes has been assembled for analysis. Usingcomputational biology and machine learning techniques, molecularinformation from population structure data are extrapolated and theinformation is used to identify the important links between microbiomeand skin age.

In another embodiment, the invention provides a method of characterizingmicrobial communities, associated enzymatic activities, and metabolitesthat can impact skin health and skin age.

In yet another embodiment, the present invention provides a method ofidentifying microbiome feature targets that influence skin age andinteractions with donor parameters like sleep, sun exposure, andantibiotic use.

In still another embodiment, the present invention provides a method fordetermining a skin age which include analyzing a microbiome of a skinsample from a donor subject and determining the skin age, whereinanalyzing includes classifying the microbiome utilizing microbiometaxonomy information.

In another embodiment, the present invention provides a method fordetermining a customized therapy. The method includes predicting and/ordetermining a skin age for a subject using the methods of the invention,and prescribing a customized treatment including, but not limited to,oral or topical medications, skin creams, lifestyle recommendations, ora combination thereof, to the subject based on the determined skin agewith, or without, the intention of improving overall skin quality.

In yet another embodiment, the present invention provides a method forrecommending a lifestyle or product based on a predicted skin age of asubject. The method includes predicting a skin age for a subject usingthe methods of the invention, and prescribing to the subject one or morelifestyle or product recommendations. In embodiments, the lifestyle orproduct recommendation is associated with prescription ornon-prescription skin care products, sun exposure limits, antibiotic use(e.g., type and quantity), sleep (e.g., daily recommended average hoursof sleep, diet, exercise (e.g., type, frequency and/or exertion level),medications, pet ownership (e.g., type of pet), probiotics (e.g., useand type), vitamin and supplement use, or combinations thereof.

In another embodiment, the invention provides a non-transitorycomputer-readable medium for predicting skin age. The medium includesinstructions stored thereon, that when executed on a processor, performthe steps of: a) analyzing microbiome data; and b) generating apredicted skin age.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphical representation of data. Forehead microbiomeprofiles of multiple individuals were compared using the Bray-Curtisdissimilarity measure. The result was demonstrated as a heat-plot withindividual microbiomes segregated to males (in blue) and females (inred) across both rows and columns, sorted in ascending age order. In theheatplot, more similar microbiomes are color-coded in red (dark grey)and less similar examples are shown in green (light grey). The red(grey) halo across the diagonal line proves that age is strong influenceon community structure.

FIG. 2 is a graphical representation of data. Two models were generatedto predict skin age based on the microbiome data (i.e. taxa only) or acombination of microbiome plus enzyme activities and metabolites. Thetaxa only (orange) predicts age correlations with actual age with aR²=0.48. Mixed model (blue) predicts age with greater accuracy andR²=0.63.

FIG. 3 is a graphical representation of data showing the effect ofdifferent environmental factors on skin age. A computational model isbuilt to predict the impact of microbiome and lifestyle parameters onskin age. The generated model can next be used to predict how any losthour of sleep or extra hour of sun-exposure ages the skin.

FIG. 4 is a graphical representation of data using Bayesian NetworkInference to identify statistically significant casual links betweendonor parameters and skin microbiome features. In the network figure,donor parameters are shown with grey nodes, bacterial taxa are shown asyellow nodes, enzyme activities are shown as purple nodes andmetabolites are shown as blue nodes. All predicted causal relationshipsbetween parameters are shown in blue.

FIG. 5 is a series of graphical representations generated using a randomforest model built to predict biological age from skin microbiome alone(left panel) or from a combination of skin microbiome, plus metadatacollected from individuals (right panel).

FIG. 6 is a graphical representation showing the importance of differentvariables deconstructed in the skin age prediction model. The y-axisshows a list of the variables (i.e. microbial species or metadata) andthe x-axis is the IncNodePurity, a measure of how impurity changes in arandom forest model when variables are randomly permuted.

DETAILED DESCRIPTION OF THE INVENTION

It is now well established that about 100 trillion microorganisms livein and on the human body vastly outnumbering the approximately 10trillion human cells. These normally harmless viruses, bacteria andfungi are referred to either as commensals (are not harmful to theirhost) or mutualistic (offer a benefit). Commensal and mutualisticorganisms help keep our bodies healthy in many ways: they help us todigest foods and acquire nutrients such as vitamins B and K, encourageour immune system to develop and prevent the colonization of, forexample, bacterial pathogens that cause disease by competing with them.Together all of the microorganisms—commensal, mutualistic andpathogenic—are referred to as the body's microbiome and theirequilibrium and associated microbiome is closely linked to anindividual's health status and vice-versa.

The present invention relates to a combination of experimental andcomputational workflows that allow characterization of specificmolecular mechanisms by which the microbiome contribute to skin healthand skin age. A skin profiling platform was used to characterize skinmicrobiome of multiple individuals at different age groups. Usingcomputational biology and machine learning techniques, molecularinformation was extrapolated from population structure data and theinformation was used to identify the important links between microbiomeand skin age. In particular, embodiments of the methods and theassociated computational platform provided herein relate to collecting aunique and highly contextualized skin microbiome dataset and generatingmetagenomic predictions and calculating metabolic models from themicrobiome community structures. Using these data, computational modelswere developed for donor age as function of donor parameters andmicrobiome features. Using this model, microbiome feature targets thatinfluence skin age and interactions with donor parameters like sleep,sun exposure, and antibiotic use were identified. Not only will thislead to specific, microbiome-based hypotheses for intervention for skinhealth, but also will become a powerful data analysis pipeline for thecomputational modeling and interpretation of future microbiome data.

The invention provides a method of identifying the specific molecularmechanisms within which microbiome contributes to skin age. To this end,a unique and richly contextualized dataset of skin microbiomes has beenassembled for analysis. Using computational biology and machine learningtechniques, molecular information from population structure data areextrapolated and the information is used to identify the important linksbetween microbiome and skin age

The term “skin” or “subcutaneous tissue” refers to the outer protectivecovering of the body, consisting of the epidermis (including the stratumcorneum) and the underlying dermis, and is understood to include sweatand sebaceous glands, as well as hair follicle structures and nails.Throughout the present application, the adjective “cutaneous” and“subcutaneous” can be used, and should be understood to refer generallyto attributes of the skin, as appropriate to the context in which theyare used. The epidermis of the human skin comprises several distinctlayers of skin tissue. The deepest layer is the stratum basalis layer,which consists of columnar cells. The overlying layer is the stratumspinosum, which is composed of polyhedral cells. Cells pushed up fromthe stratum spinosum are flattened and synthesize keratohyalin granulesto form the stratum granulosum layer. As these cells move outward, theylose their nuclei, and the keratohyalin granules fuse and mingle withtonofibrils. This forms a clear layer called the stratum lucidum. Thecells of the stratum lucidum are closely packed. As the cells move upfrom the stratum lucidum, they become compressed into many layers ofopaque squamae. These cells are all flattened remnants of cells thathave become completely filled with keratin and have lost all otherinternal structure, including nuclei. These squamae constitute the outerlayer of the epidermis, the stratum corneum. At the bottom of thestratum corneum, the cells are closely compacted and adhere to eachother strongly, but higher in the stratum they become loosely packed,and eventually flake away at the surface.

As used in this specification and the appended claims, the singularforms “a”, “an”, and “the” include plural references unless the contextclearly dictates otherwise. Thus, for example, references to “themethod” includes one or more methods, and/or steps of the type describedherein which will become apparent to those persons skilled in the artupon reading this disclosure and so forth.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the invention, the preferred methods andmaterials are now described.

The invention relates generally to using microbiome community structuresto predict microbiome metagenomes, the genes and gene abundances presentin a microbial community. Predicted metagenomes are comprised of 2055enzyme functions. From predicted metagenomes, community metabolomes aremodeled. Model community metabolomes are comprised of 2893 metabolites,4481 enzyme function-mediated interactions, and 1346 enzyme functions.From prior experimental result, predicted metagenomes and metabolomecorrelate well with biological observations.

To further characterize the association of age with microbiome, theforehead microbiome profiles of multiple individuals were compared usingthe Bray-Curtis dissimilarity measure. The result was demonstrated as aheat-plot with individual microbiomes segregated to males (in blue) andfemales (in red), sorted in ascending age order. In the heatplot, moresimilar microbiomes are color-coded in red and less similar examples areshown in green. The red halo across the diagonal line proves that age isstrong influence on community structure.

A machine-learning approach was used to generate computational modelsthat predict donor age as a function of donor parameters (e.g. gender,ethnicity, hours of sleep, hours of sun exposure) and microbiomefeatures (population structure, predicted metagenome). To this end, astatistics-based evolutional algorithm using symbolic regression wasused to search the space of mathematical equations to find a model thatbest fits the data provided, varying both the form and parameters ofpossible models. Two models were generated. The first used onlypopulation structure data for microbiome features, the secondincorporated predicted metagenomes and models metabolome in addition topopulation structure. All the microbiome features, lifestyle informationand parameters were collected from human subjects.

The term “subject” as used herein refers to any individual or patient towhich the subject methods are performed. Generally the subject is human,although as will be appreciated by those in the art, the subject may bean animal. Thus other animals, including mammals such as rodents(including mice, rats, hamsters and guinea pigs), cats, dogs, rabbits,farm animals including cows, horses, goats, sheep, pigs, etc., andprimates (including monkeys, chimpanzees, orangutans and gorillas) areincluded within the definition of subject.

Computational models were validated in one of two ways. The firstvalidation method, the correlation between predicted and actual donorage was considered (FIG. 2). Here, the mixed model (Pearson CorrelationCoefficient=0.81) outperformed the taxa-only model (Pearson CorrelationCoefficient=0.72). While both predictions are strong, the mixed modelhas the advantage of not only being more accurate, but also the mixedmodel has the capacity to provide greater insight into the molecularmechanisms than link skin microbiome with skin age. In the secondvalidation method, we predicted the effects of reduced sleep, increasingsun exposure, and use of antibiotics of skin “age” (FIG. 3). Asexpected, these parameters has a negative effect of predicted skin age.As predicted from computational model, every extra hour of lost sleep“ages” skin ˜0.98 years, every extra hour of sun “ages” skin ˜0.46years, and the use of antibiotics “ages” skin ˜0.54 years.

Accordingly, in one aspect, the invention provides a method ofcharacterizing the age of skin or subcutaneous tissue of a subject. Themethod includes: a) obtaining a sample comprising a plurality ofmicroorganisms from the skin or subcutaneous tissue of the subject; andb) analyzing and classifying the plurality of microorganisms of (a) tocharacterize the microbiome of the subject, thereby characterizing themicrobiome of the subject; and c) use the microbiome information topredict skin or subcutaneous tissue age.

As used herein, the terms “sample” and “biological sample” refer to anysample suitable for the methods provided by the present invention. Asample of cells can be any sample, including, for example, a skin orsubcutaneous tissue sample obtained by non-invasive techniques such astape stripping, scraping, swabbing, or more invasive techniques such asbiopsy of a subject. In one embodiment, the term “sample” refers to anypreparation derived from skin or subcutaneous tissue of a subject. Forexample, a sample of cells obtained using the non-invasive methoddescribed herein can be used to isolate nucleic acid molecules orproteins for the methods of the present invention. Samples for thepresent invention may be taken from an area of the skin shown to exhibita disease or disorder, which is suspected of being the result of adisease or a pathological or physiological state, such as psoriasis ordermatitis, or the surrounding margin or tissue. As used herein,“surrounding margin” or “surrounding tissue” refers to tissue of thesubject that is adjacent to the skin shown to exhibit a disease ordisorder, but otherwise appears to be normal.

Accordingly, in one aspect, the invention proposes a model thatdescribes how the microbiome can potentially protect skin from agingeffects. A Baysian Network (BN) model is generated for donor parametersand microbiome features to identify the potential causal links betweenthem. BN are probabilistic graphical models of conditional dependenciesbetween random variables in the form of a directed acyclic graph.Directed edges are relationships between nodes inferred from data suchthat the state of a child node is dependent on the states of its parentnodes. In the generated network, no node was permitted to be the parentnode of a donor parameter. Networks for microbiome taxa, predictedmetagenomes, and community metabolomes were generated independently, andthen final networks were combined into a composite network (FIG. 4).From this network, a number of potential molecular mechanisms can bepredicted, linked to antioxidant activities, antimicrobial activities,and production of anti-inflammatory compounds.

Accordingly, in one aspect, the invention proposes a model that canpredict skin age from skin microbiome composition. The model has beenbuilt using a random forest approach that can take the microbiomecomposition as the only input (FIG. 5, left panel) with a R-squaredvalue of 0.89. The model can be improved further by including othermetadata including average hours of sun exposure, average hours ofsleep, skin microbiome balance, skin microbiome diversity, and skinhappiness. The new model which includes the microbiome composition andall above-mentioned metadata (FIG. 5, right panel) has an improvedperformance with a R-squared value of 0.93.

Contribution of different variables, microbial species, microbialgenera, or metadata to skin age can be deconstructed from the model(FIG. 6). The list of variables in the order of their contribution toskin age model are listed in the y-axis from top to bottom. As shown inthe graph, Corynebacterium kroppenstedtii, hours of sleep,Propionibacterium acnes, Neisseria meningitides, and Staphylococcusepidermidis are the top variables with maximum contribution to thepredicted skin age.

Accordingly, in one aspect, the invention provides a method ofcharacterizing skin age for healthy or disease samples.

As used herein “healthy” refers to a sample from a subject that is freefrom disease or disorder, a skin disorder, any particular undesirablephenotype or risk thereof. The term healthy skin refers to skin that isdevoid of a skin condition, disease or disorder, including, but notlimited to inflammation, rash, dermatitis, atopic dermatitis, eczema,psoriasis, dandruff, acne, cellulitis, rosacea, warts, seborrheickeratosis, actinic keratosis, tinea versicolor, viral exantham,shingles, ringworm, and cancer, such as basal cell carcinoma, squamouscell carcinoma, and melanoma.

Additionally, as used herein, a “disease” or “disorder” is intended togenerally refer to any skin associated disease, for example, but in noway limited to, inflammation, rash, dermatitis, atopic dermatitis,eczema, psoriasis, dandruff, acne, cellulitis, rosacea, warts,seborrheic keratosis, actinic keratosis, tinea versicolor, viralexantham, shingles, ringworm, and cancer, such as basal cell carcinoma,squamous cell carcinoma, and melanoma.

The term “cancer” as used herein, includes any malignant tumorincluding, but not limited to, carcinoma, melanoma and sarcoma. Cancerarises from the uncontrolled and/or abnormal division of cells that theninvade and destroy the surrounding tissues. As used herein,“proliferating” and “proliferation” refer to cells undergoing mitosis.As used herein, “metastasis” refers to the distant spread of a malignanttumor from its sight of origin. Cancer cells may metastasize through thebloodstream, through the lymphatic system, across body cavities, or anycombination thereof. The term “cancerous cell” as provided herein,includes a cell afflicted by any one of the cancerous conditionsprovided herein. The term “carcinoma” refers to a malignant new growthmade up of epithelial cells tending to infiltrate surrounding tissues,and to give rise to metastases. The term “melanoma” refers to amalignant tumor of melanocytes which are found predominantly in skin butalso in bowel and the eye. “Melanocytes” refer to cells located in thebottom layer, the basal lamina, of the skin's epidermis and in themiddle layer of the eye. Thus, “melanoma metastasis” refers to thespread of melanoma cells to regional lymph nodes and/or distant organs(e.g., liver, brain, breast, prostate, etc.).

The microbiome profiles can be generated by any method and platform thatutilizes analysis of a nucleic acid molecule, such as sequencing anucleic acid molecule. Sequencing methods may include whole genomesequencing, next generation sequencing, Sanger-sequencing, 16S rDNAsequencing and 16S rRNA sequencing. Further, such methods and platformsdescribed herein may utilize mass-spectrometry, quantitative PCR,immunofluorescence, in situ hybridization, a microbial staining basedplatform, or combination thereof.

In embodiments, the input to the identification platform can be anynucleic acid, including DNA, RNA, cDNA, miRNA, mtDNA, single ordouble-stranded. This nucleic acid can be of any length, as short asoligos of about 5 bp to as long a megabase or even longer. As usedherein, the term “nucleic acid molecule” means DNA, RNA,single-stranded, double-stranded or triple stranded and any chemicalmodifications thereof. Virtually any modification of the nucleic acid iscontemplated. A “nucleic acid molecule” can be of almost any length,from 10, 20, 30, 40, 50, 60, 75, 100, 125, 150, 175, 200, 225, 250, 275,300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500,4000, 4500, 5000, 6000, 7000, 8000, 9000, 10,000, 15,000, 20,000,30,000, 40,000, 50,000, 75,000, 100,000, 150,000, 200,000, 500,000,1,000,000, 1,500,000, 2,000,000, 5,000,000 or even more bases in length,up to a full-length chromosomal DNA molecule. For methods that analyzeexpression of a gene, the nucleic acid isolated from a sample istypically RNA.

Micro-RNAs (miRNA) are small single stranded RNA molecules an average of22 nucleotides long that are involved in regulating mRNA expression indiverse species including humans (reviewed in Bartel 2004). The firstreport of miRNA was that of the lin-4 gene, discovered in the worm C.elegans (Lee, Feinbaum et al. 1993). Since then hundreds of miRNAs havebeen discovered in flies, plants and mammals. miRNAs regulate geneexpression by binding to the 3′-untranslated regions of mRNA andcatalyze either i) cleavage of the mRNA; or 2) repression oftranslation. The regulation of gene expression by miRNAs is central tomany biological processes such as cell development, differentiation,communication, and apoptosis (Reinhart, Slack et al. 2000; Baehrecke2003; Brennecke, Hipfner et al. 2003; Chen, Li et al. 2004). Recently ithas been shown that miRNA are active during embryogenesis of the mouseepithelium and play a significant role in skin morphogenesis (Yi,O'Carroll et al. 2006).

Given the role of miRNA in gene expression it is clear that miRNAs willinfluence, if not completely specify the relative amounts of mRNA inparticular cell types and thus determine a particular gene expressionprofile (i.e., a population of specific mRNAs) in different cell types.In addition, it is likely that the particular distribution of specificmiRNAs in a cell will also be distinctive in different cell types. Thus,determination of the miRNA profile of a tissue may be used as a tool forexpression profiling of the actual mRNA population in that tissue.Accordingly, miRNA levels and/or detection of miRNA mutations are usefulfor the purposes of disease detection, diagnosis, prognosis, ortreatment-related decisions (i.e., indicate response either before orafter a treatment regimen has commenced) or characterization of aparticular disease in the subject.

As used herein, the term “protein” refers to at least two covalentlyattached amino acids, which includes proteins, polypeptides,oligopeptides and peptides. A protein may be made up of naturallyoccurring amino acids and peptide bonds, or synthetic peptidomimeticstructures. Thus “amino acid”, or “peptide residue”, as used hereinmeans both naturally occurring and synthetic amino acids. For example,homo-phenylalanine, citrulline and noreleucine are considered aminoacids for the purposes of the invention. “Amino acid” also includesimino acid residues such as proline and hydroxyproline. The side chainsmay be in either the (R) or the (S) configuration.

A “probe” or “probe nucleic acid molecule” is a nucleic acid moleculethat is at least partially single-stranded, and that is at leastpartially complementary, or at least partially substantiallycomplementary, to a sequence of interest. A probe can be RNA, DNA, or acombination of both RNA and DNA. It is also within the scope of thepresent invention to have probe nucleic acid molecules comprisingnucleic acids in which the backbone sugar is other that ribose ordeoxyribose. Probe nucleic acids can also be peptide nucleic acids. Aprobe can comprise nucleolytic-activity resistant linkages or detectablelabels, and can be operably linked to other moieties, for example apeptide.

A single-stranded nucleic acid molecule is “complementary” to anothersingle-stranded nucleic acid molecule when it can base-pair (hybridize)with all or a portion of the other nucleic acid molecule to form adouble helix (double-stranded nucleic acid molecule), based on theability of guanine (G) to base pair with cytosine (C) and adenine (A) tobase pair with thymine (T) or uridine (U). For example, the nucleotidesequence 5′-TATAC-3′ is complementary to the nucleotide sequence5′-GTATA-3′.

As used herein “hybridization” refers to the process by which a nucleicacid strand joins with a complementary strand through base pairing.Hybridization reactions can be sensitive and selective so that aparticular sequence of interest can be identified even in samples inwhich it is present at low concentrations. In an in vitro situation,suitably stringent conditions can be defined by, for example, theconcentrations of salt or formamide in the prehybridization andhybridization solutions, or by the hybridization temperature, and arewell known in the art. In particular, stringency can be increased byreducing the concentration of salt, increasing the concentration offormamide, or raising the hybridization temperature. For example,hybridization under high stringency conditions could occur in about 50%formamide at about 37° C. to 42° C. Hybridization could occur underreduced stringency conditions in about 35% to 25% formamide at about 30°C. to 35° C. In particular, hybridization could occur under highstringency conditions at 42° C. in 50% formamide, 5×SSPE, 0.3% SDS, and200 mg/ml sheared and denatured salmon sperm DNA. Hybridization couldoccur under reduced stringency conditions as described above, but in 35%formamide at a reduced temperature of 35° C. The temperature rangecorresponding to a particular level of stringency can be furthernarrowed by calculating the purine to pyrimidine ratio of the nucleicacid of interest and adjusting the temperature accordingly. Variationson the above ranges and conditions are well known in the art.

As used herein, the term “skin flora” or “microbiome” refers tomicroorganisms, including bacteria, viruses, and fungi that inhabit theskin or subcutaneous tissues of the subject.

As used herein, the terms microbial, microbe, or microorganism refer toany microscopic organism including prokaryotes or eukaryotes, bacterium,archaebacterium, fungus, virus, or protist, unicellular ormulticellular.

As used herein, the term “ameliorating” or “treating” means that theclinical signs and/or the symptoms associated with the cancer ormelanoma are lessened as a result of the actions performed. The signs orsymptoms to be monitored will be characteristic of a particular canceror melanoma and will be well known to the skilled clinician, as will themethods for monitoring the signs and conditions. Thus, a “treatmentregimen” refers to any systematic plan or course for treating a diseaseor cancer in a subject.

In embodiments, nucleic acid molecules can also be isolated by lysingthe cells and cellular material collected from the skin sample by anynumber of means well known to those skilled in the art. For example, anumber of commercial products available for isolating polynucleotides,including but not limited to, RNeasy™ (Qiagen, Valencia, Calif.) andTriReagent™ (Molecular Research Center, Inc, Cincinnati, Ohio) can beused. The isolated polynucleotides can then be tested or assayed forparticular nucleic acid sequences, including a polynucleotide encoding acytokine. Methods of recovering a target nucleic acid molecule within anucleic acid sample are well known in the art, and can includemicroarray analysis.

As discussed further herein, nucleic acid molecules may be analyzed inany number of ways known in the art that may assist in determining themicrobiome and/or metabolome associated with an individual's skin. Forexample, the presence of nucleic acid molecules can be detected byDNA-DNA or DNA-RNA hybridization or amplification using probes orfragments of the specific nucleic acid molecule. Nucleic acidamplification based assays involve the use of oligonucleotides oroligomers based on the nucleic acid sequences to detect transformantscontaining the specific DNA or RNA.

In another embodiment, antibodies that specifically bind the expressionproducts of the nucleic acid molecules of the invention may be used tocharacterize the skin lesion of the subject. The antibodies may be usedwith or without modification, and may be labeled by joining them, eithercovalently or non-covalently, with a reporter molecule.

A wide variety of labels and conjugation techniques are known by thoseskilled in the art and may be used in various nucleic acid and aminoacid assays. Means for producing labeled hybridization or PCR probes fordetecting sequences related to the nucleic acid molecules of Tables 1-6include oligolabeling, nick translation, end-labeling or PCRamplification using a labeled nucleotide. Alternatively, the nucleicacid molecules, or any fragments thereof, may be cloned into a vectorfor the production of an mRNA probe. Such vectors are known in the art,are commercially available, and may be used to synthesize RNA probes invitro by addition of an appropriate RNA polymerase such as T7, T3, orSP6 and labeled nucleotides. These procedures may be conducted using avariety of commercially available kits (Pharmacia & Upjohn, (Kalamazoo,Mich.); Promega (Madison Wis.); and U.S. Biochemical Corp., Cleveland,Ohio). Suitable reporter molecules or labels, which may be used for easeof detection, include radionuclides, enzymes, fluorescent,chemiluminescent, or chromogenic agents as well as substrates,cofactors, inhibitors, magnetic particles, and the like.

PCR systems usually use two amplification primers and an additionalamplicon-specific, fluorogenic hybridization probe that specificallybinds to a site within the amplicon. The probe can include one or morefluorescence label moieties. For example, the probe can be labeled withtwo fluorescent dyes: 1) a 6-carboxy-fluorescein (FAM), located at the5′-end, which serves as reporter, and 2) a6-carboxy-tetramethyl-rhodamine (TAMRA), located at the 3′-end, whichserves as a quencher. When amplification occurs, the 5′-3′ exonucleaseactivity of the Taq DNA polymerase cleaves the reporter from the probeduring the extension phase, thus releasing it from the quencher. Theresulting increase in fluorescence emission of the reporter dye ismonitored during the PCR process and represents the number of DNAfragments generated. In situ PCR may be utilized for the directlocalization and visualization of target nucleic acid molecules and maybe further useful in correlating expression with histopathologicalfinding.

Means for producing specific hybridization probes for nucleic acidmolecules of the invention include the cloning of the nucleic acidsequences into vectors for the production of mRNA probes. Such vectorsare known in the art, commercially available, and may be used tosynthesize RNA probes in vitro by means of the addition of theappropriate RNA polymerases and the appropriate labeled nucleotides.Hybridization probes may be labeled by a variety of reporter groups, forexample, radionuclides such as 32P or 35S, or enzymatic labels, such asalkaline phosphatase coupled to the probe via avidin/biotin couplingsystems, and the like.

The term “skin care product” or “personal care product” refers to skincare products and includes, but is not limited to, cleansing products,shampoo, conditioner, toners or creams, topical ointments and gels, aswell as localized (e.g. under eye) gel, all of which may be formulatedto contain ingredients specifically designed to shift microbialpopulation to a healthy profile with or without a commensal ormutualistic organism or mixture of commensal or mutualistic organisms ineither an active or dormant state. Such skin care products may furtherinclude therapeutic agents, vitamins, antioxidants, minerals, skintoning agents, polymers, excipients, surfactants, probiotics or fractionthereof, microorganism or product from the culture thereof, such abacteria, fungi and the like, either living, dormant or inactive.

“Skin commensal microorganisms” means both prokaryotes and eukaryotesthat may colonize (i.e., live and multiply on human skin) or temporarilyinhabit human skin in vitro, ex vivo and/or in vivo. Exemplary skincommensal microorganisms include, but are not limited to,Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria,Propionibacteria, Corynebacteria, Actinobacteria, Clostridiales,Lactobacillales, Staphylococcus, Bacillus, Micrococcus, Streptococcus,Bacteroidales, Flavobacteriales, Enterococcus, Pseudomonas, Malassezia,Maydida, Debaroyomyces, and Cryptococcus.

P. acnes is a commensal, non-sporulating bacilliform (rod-shaped),gram-positive bacterium found in a variety of locations on the humanbody including the skin, mouth, urinary tract and areas of the largeintestine. P. acnes can consume skin oil and produce byproducts such asshort-chain fatty acids and propionic acid, which are known to helpmaintain a healthy skin barrier. Propionibacteria such as P. acnes alsoproduce bacteriocins and bacteriocin-like compounds (e.g., propionicinP1G-1, jenseniin G, propionicins SM1, SM2 T1, and acnecin), which areinhibitory toward undesirable lactic acid-producing bacteria,gram-negative bacteria, yeasts, and molds. In embodiments, a subjecthaving skin identified as having P. acnes may be treated with a personalcare product designed to inhibit growth and proliferation of P. acnes.

In an embodiment, the invention provides a method of characterizing skinand/or subcutaneous tissue comprising collecting a sample from a subjectcontaining skin or subcutaneous tissue flora. Skin and subcutaneoustissue flora of healthy individuals can be collected using swiping,scraping, swabbing, using tape strips or any other effective microbialcollection method. The harvested sample can be profiled on a NGS,Sanger-sequencing, mass-spectrometry, quantitative PCR,immunofluorescence, in situ hybridization, or microbial staining basedplatform. For sequencing-based platforms, this can be done either usinga whole-genome sequencing approach, or via targeted applications, aprominent example of which is 16S rDNA sequencing. All theabove-mentioned identification methods can be implemented on samplesdirectly collected from individuals without any proliferation step. Thisway, minimal bias is introduced toward identification of a mixture ofculturable and unculturable microorganisms. A proprietary analysisalgorithm can be used to identify species composition of eachindividual. A consensus healthy profile may be constructed from thehealthy cohort. The healthy profile may be updated real time as moresamples are collected over time. The healthy profile will serve as thereference for comparing all individual samples, i.e. profiles. Examplesof identified bacteria belong to any phylum, including Actinobacteria,Firmicutes, Proteobacteria, Bacteroidetes. It will typically includecommon species such as Propionibacteria, Staphylococci, Corynebacteria,and Acenitobacteria species.

In an embodiment, the invention provides a platform or method forcharacterizing skin and subcutaneous tissue microbial flora ofindividuals with skin conditions. Skin and subcutaneous tissue flora ofindividuals with skin conditions that are considered to be suboptimalcan be collected using swiping, swabbing, tape strips or any othereffective microbial collection method. Collected microbial sample can beprofiled on a NGS, Sanger-sequencing, mass-spectrometry, quantitativePCR, immunofluorescence, in situ hybridization, or microbial stainingbased platform. For the sequencing based platforms, this can be doneeither using a whole-genome sequencing approach, or via targetedapplications, a prominent example of which is 16S rDNA sequencing. Allthe identification methods can be implemented on samples directlycollected from individuals without any proliferation step. This way,minimal bias is introduced toward identification of a mixture ofculturable and unculturable microorganisms. A personal skin andsubcutaneous tissue flora profile can be generated for each individual.Individuals, based on their phenotypic characteristics, can be placedunder specific skin condition categories as well. Such clustering effortwill help to identify biological significant patterns which arecharacteristic of each cohort. The microbial composition of the affectedcohort is distinct from the healthy profile. Microbial species which areassociated with any given skin condition can be used as early diagnosticmarkers for individuals who have not developed a visual skin conditionbut may be prone to that. Examples of identified bacteria belong to anyphylum, including Actinobacteria, Firmicutes, Proteobacteria,Bacteroidetes. It will typically include common species, such asPropionibacteria, Staphylococci, Corynebacteria, and Acenitobacteriaspecies. Damaged skin can impact the composition of bacterial flora orcan cause nonpathogenic bacteria to become pathogenic.

In an embodiment, the invention provides a platform or method forcharacterizing a consensus healthy skin and subcutaneous tissuemetabolite profile. The metabolome associated with skin and subcutaneoustissue flora can also be profiled either by a mass-spectrometry basedsystem or using genomics-based metabolome modeling and flux-balanceanalysis. Extraction can be done on samples collected by using swiping,swabbing, tape strips or any other effective microbial collectionmethod. Alternatively, those metabolites and biochemical, specificallythe extracellular ones, can be directly isolated from any individualwithout going through any cell harvesting. Characterization can be doneon the whole metabolome or only be focused on a subset of metabolites,which are known or may be shown to be of significance in a particulardisease pathology. All the above-mentioned identification methods can beimplemented on samples directly collected from individuals without anyproliferation step. This way, minimal bias is introduced in thepopulation composition. A proprietary analysis algorithm may be used toidentify metabolite composition of each individual's skin flora. Aconsensus healthy profile may be constructed from the healthy cohort.The healthy profile may be updated real time as more samples arecollected over time. The healthy profile will serve as the reference forcomparing all individual samples, i.e. profiles.

In an embodiment, the invention provides a platform or method forcharacterizing skin and subcutaneous tissue microbial flora ofindividuals with skin conditions. Metabolite composition of skin andsubcutaneous tissue flora of individuals with skin conditions that areconsidered to be suboptimal can be profiled either by amass-spectrometry based system or using genomics-based metabolomemodeling and flux-balance analysis. Extraction can be done on samplescollected by using swiping, swabbing, tape strips or any other effectivemicrobial collection method. Alternatively, those metabolites andbiochemical, specifically the extracellular ones, can be directlyisolated from any individual without going through any cell harvesting.Characterization can be done on the whole metabolome or only be focusedon a subset of metabolites, which are known or may be shown to be ofsignificance. All the above-mentioned identification methods can beimplemented on samples directly collected from individuals without anyproliferation step. This way, minimal bias is introduced in thepopulation composition. A personal profile can be generated for eachindividual that reflects the metabolite composition of the skin andsubcutaneous tissue flora. Individuals, based on their phenotypiccharacteristics, can be placed under specific skin condition categoriesas well. Such clustering effort will help to identify biologicalsignificant patterns that are characteristic of each cohort. Themetabolite composition of the affected cohort is distinct from thehealthy profile. Metabolites which are associated with any given skincondition can be used as early diagnostic markers for individuals whohave not developed a visual skin condition but may be prone to that.

In an embodiment, the platform or method described herein may beprovided as a test for profiling the skin flora of any individual,either healthy or with a skin condition and also their associatedmetabolome. Such test would be sensitive to characterize the dominantskin flora and metabolites of any individual. A customized orpersonalized evaluation of any individual's flora may be conducted andidentified skin and subcutaneous tissue flora and metabolites may becompared to healthy and also affected skin profiles. A customized orpersonalized report may be generated which will specify speciescomposition of the individual's skin and subcutaneous tissue flora andalso its associated metabolites. Such report will enlist the beneficialand commensal species that are depleted or over-represented in eachindividual. It will also include the list of beneficial or undesiredmetabolites that are either depleted or over-represented in eachindividual. This may be used for formulation of the customized orpersonalized skin care or personal care product. Such report may alsoform the basis of a recommendation engine that generates clinical,lifestyle and product recommendations that may improve the health of theskin either directly as a result of a change in the diversity orcomposition of an individual's skin flora, or a change in both diversityand composition of an individual's skin flora. Alternatively, the testcan be administered to assess the performance of other skin care andpersonal care products, therapies, or evaluate any disruption of thenormal skin flora or metabolites. The test can be performed before,during, and after any skin treatment in order to monitor the efficacy ofthat treatment regimen on skin flora or its associated metabolites. Thetest can also be used for early diagnostic of skin conditions that areassociated with a signature microbial profile or their accompanyingmetabolites. The sensitivity of the test allows early diagnostic of suchskin conditions before their phenotypic outbreak. In an aspect, theinvention provides a method for generating, or a customized orpersonalized skin care or personal care product formulated for aparticular individual. The customized or personalized product containsone or more beneficial or commensal microorganisms or a set of chemicalsand metabolites which may be depleted in any given individual. Regularadministration of such skin care products and personal care productsshould shift the suboptimal profile towards a healthy equilibrium. Skincare product may be applied after cleansing the existing flora with aproprietary lotion that will enhance the efficacy of colonization ofskin care product microorganisms or its constituent metabolites. Anycustomized or personalized skin care or personal care product cancontain one or more microorganisms, culturable or unculturable. Thecustomized or personalized product can alternatively be a substrate andnutrients that favor the establishment or proliferation of mutualisticor commensal organisms and/or suppression of pathogenic organisms. Thosechemicals and metabolites are either synthesized in vitro or purifiedfrom a microorganism.

The present invention is described partly in terms of functionalcomponents and various processing steps. Such functional components andprocessing steps may be realized by any number of components, operationsand techniques configured to perform the specified functions and achievethe various results. For example, the present invention may employvarious biological samples, biomarkers, elements, materials, computers,data sources, storage systems and media, information gatheringtechniques and processes, data processing criteria, statisticalanalyses, regression analyses and the like, which may carry out avariety of functions. In addition, although the invention is describedin the medical diagnosis context, the present invention may be practicedin conjunction with any number of applications, environments and dataanalyses; the systems described herein are merely exemplary applicationsfor the invention.

Methods for data analysis according to various aspects of the presentinvention may be implemented in any suitable manner, for example using acomputer program operating on the computer system. An exemplary analysissystem, according to various aspects of the present invention, may beimplemented in conjunction with a computer system, for example aconventional computer system comprising a processor and a random accessmemory, such as a remotely-accessible application server, networkserver, personal computer or workstation. The computer system alsosuitably includes additional memory devices or information storagesystems, such as a mass storage system and a user interface, for examplea conventional monitor, keyboard and tracking device. The computersystem may, however, comprise any suitable computer system andassociated equipment and may be configured in any suitable manner. Inone embodiment, the computer system comprises a stand-alone system. Inanother embodiment, the computer system is part of a network ofcomputers including a server and a database.

The software required for receiving, processing, and analyzing biomarkerinformation may be implemented in a single device or implemented in aplurality of devices. The software may be accessible via a network suchthat storage and processing of information takes place remotely withrespect to users. The analysis system according to various aspects ofthe present invention and its various elements provide functions andoperations to facilitate microbiome analysis, such as data gathering,processing, analysis, reporting and/or diagnosis. The present analysissystem maintains information relating to microbiomes and samples andfacilitates analysis and/or diagnosis, For example, in the presentembodiment, the computer system executes the computer program, which mayreceive, store, search, analyze, and report information relating to themicrobiome. The computer program may comprise multiple modulesperforming various functions or operations, such as a processing modulefor processing raw data and generating supplemental data and an analysismodule for analyzing raw data and supplemental data to generate a modelsand/or predictions.

The epigenetic analysis system may also provide various additionalmodules and/or individual functions. For example, the epigeneticanalysis system may also include a reporting function, for example toprovide information relating to the processing and analysis functions.The epigenetic analysis system may also provide various administrativeand management functions, such as controlling access and performingother administrative functions.

Although the invention has been described with reference to the aboveexamples, it will be understood that modifications and variations areencompassed within the spirit and scope of the invention. Accordingly,the invention is limited only by the following claims.

What is claimed is:
 1. A method for treating an individual based on askin age of the individual, comprising: (a) receiving metadata relatedto the individual and a microbiome of the individual, wherein themetadata comprises a parameter selected from the group consisting oflifestyle information of the individual, an ethnicity of the individual,a duration of sleep of the individual, a duration of sun exposure of theindividual, a diet of the individual, an antibiotic used by theindividual, and a skin care product used by the individual; (b)processing the metadata related to the individual and the microbiome ofthe individual using a machine learning model to determine the skin ageof the individual, wherein the machine learning model is configured todetermine skin age based on a combination of microbiome and metadatacomprising the parameter, wherein the machine learning model isconstructed using population skin ages and population metadata; (c)determining, based at least in part on the skin age of the individual, atreatment to provide to the individual to improve the skin age of theindividual; and (d) treating the individual with the treatment.
 2. Themethod of claim 1, wherein the metadata further comprises a gender ofthe individual.
 3. The method of claim 1, wherein the treatmentcomprises a modification of the lifestyle of the individual, amodification of the duration of sleep of the individual, a modificationof the duration of sun exposure of the individual, a modification of thediet of the individual, a modification of the antibiotic used by theindividual, or a modification of the skin care product used by theindividual.
 4. The method of claim 1, wherein the skin age of theindividual is different from a chronological age of the individual. 5.The method of claim 1, further comprising obtaining a sample from theindividual, and assaying the sample to determine the microbiome of theindividual.
 6. The method of claim 5, wherein the sample is obtainedfrom a skin or subcutaneous tissue of the individual via swipe, scrape,swab, biopsy, or tape.
 7. The method of claim 5, wherein the microbiomecomprises a plurality of microorganisms selected from the groupconsisting of bacteria, fungi, and any combination thereof.
 8. Themethod of claim 5, wherein the microbiome comprises a bacteria or afungi selected from the group consisting of Alphaproteobacteria,Betaproteobacteria, Gammaproteobacteria, Propionibacteria,Proteobacteria, Bacteroidetes, Corynebacteria, Actinobacteria,Clostridiales, Lactobacillales, Staphylococcus, Bacillus, Micrococcus,Streptococcus, Bacteroidales, Flavobacteriales, Firmicutes,Enterococcus, Pseudomonas, Malassezia, Maydida, Debaroyomyces, andCryptococcus.
 9. The method of claim 5, wherein the microbiome comprisesa bacteria of the genus Propionibacteria, Staphylococci, Corynebacteria,or Acenitobacteria.
 10. The method of claim 5, wherein the microbiomecomprises a bacteria selected from the group consisting ofPropionibacterium acnes, Corynebacterium kroppenstedtii, Neisseriameningitides, and Staphylococcus epidermidis.
 11. The method of claim 5,wherein the assaying comprises sequencing nucleic acids of the sample todetermine the microbiome.
 12. The method of claim 11, wherein thesequencing is selected from the group consisting of whole genomesequencing, next-generation sequencing, Sanger-sequencing, 16S ribosomaldeoxyribonucleic acid (rDNA) sequencing, and 16S ribosomal ribonucleicacid (rRNA) sequencing.
 13. The method of claim 1, wherein the treatmentcomprises a substance that modifies a microbiome of the individual. 14.The method of claim 1, wherein determining the treatment to provide tothe individual comprises determining an effect of an antibioticcompound, an antioxidant compound, or an anti-inflammatory compound on amicrobiome of the individual.
 15. The method of claim 14, wherein thetreatment comprises the antibiotic compound, the antioxidant compound,or the anti-inflammatory compound that is determined to improve the skinage of the individual.
 16. The method of claim 15, wherein the treatmentcomprises the antibiotic compound, the antioxidant compound, or theanti-inflammatory compound that is determined to improve the skin age ofthe individual along with another recommendation to improve the skin ageof the individual.
 17. The method of claim 1, wherein the machinelearning model comprises a regression model.
 18. The method of claim 1,wherein the machine learning model comprises a Bayesian network model.19. The method of claim 1, wherein the machine learning model comprisesa random forest model.
 20. The method of claim 19, wherein the randomforest model determines the skin age of the individual based at least inpart on analyzing at least one feature of microbial species or metadataselected from the group listed in FIG.
 6. 21. The method of claim 20,wherein the random forest model determines the skin age of theindividual based at least in part on analyzing at least 5 features ofmicrobial species or metadata selected from the group listed in FIG. 6.22. The method of claim 1, wherein the parameter comprises the durationof sleep of the individual, the duration of sun exposure of theindividual, the diet of the individual, the antibiotic used by theindividual, or the skin care product used by the individual.
 23. Themethod of claim 22, wherein the parameter comprises the duration ofsleep of the individual or the duration of sun exposure of theindividual.
 24. The method of claim 23, wherein the parameter comprisesthe duration of sleep of the individual.
 25. The method of claim 24,wherein the parameter comprises the duration of sleep of the individualand the duration of sun exposure of the individual.